Positioning in 5G and 6G Networks-A Survey.

Area of Science:

• Electrical Engineering
• Computer Science
• Telecommunications

Background:

• Accurate positioning is crucial, but real-time indoor location tracking remains a challenge.
• Traditional methods like GPS are insufficient for indoor environments.
• The advent of 5G mobile networks presents new opportunities for network-based positioning.

Purpose of the Study:

• To provide a comprehensive overview of network-based positioning, from fundamental concepts to advanced machine learning solutions.
• To compare various machine learning techniques employed in network-based positioning.
• To explore the potential of 6G networks for enhanced positioning capabilities.

Main Methods:

• Review of existing literature on network-based positioning techniques.
• Analysis of 5G and emerging 6G network features relevant to positioning.
• Comparative study of machine learning algorithms applied to positioning data.
• Examination of application scenarios in industrial and vehicular domains.

Main Results:

• 5G networks offer enhanced capabilities for both indoor and outdoor positioning due to advanced radio technologies, low latency, and edge computing.
• Machine learning significantly improves the accuracy and efficiency of network-based positioning systems.
• A detailed comparison of different machine learning techniques highlights their respective strengths for positioning tasks.
• Emerging 6G networks are poised to further revolutionize positioning accuracy and scope.

Conclusions:

• Network-based positioning, particularly with 5G and 6G, is a viable and evolving solution for real-time location services.
• Machine learning is integral to achieving state-of-the-art performance in network-based positioning.
• The presented findings provide valuable insights for researchers and practitioners in industrial and vehicular applications.